Biometric attribute anomaly detection system with adjusting notifications

ABSTRACT

A system, methods and server for monitoring health and safety of individuals in a population and sending alert notifications when exceptions are detected include comparing biometric data obtained from the individuals to a biometric model generated for the individual through computer-learning methods. Biometric data may be gathered by wireless biometric sensor devices which transmit biometric data to receiver devices, which relay the biometric data to a server. The biometric model may be maintained in the server and include nominal and threshold biometric parameters for each individual based on biometric sensor data gathered or analyzed over a period of time. An alert may be issued by the server when an individual&#39;s biometric data is outside a threshold in the biometric model. The transmitted alert may depend upon the nature of the exception, user settings and past notification experience. Alerts may be escalated when not answered within defined durations.

RELATED APPLICATIONS

This application claims the benefit of priority to U.S. PatentApplication No. 61/625,778 entitled “Biometric Attribute AnomolyDetection System With Adjusting Notifications” filed Apr. 18, 2012, theentire contents of which are hereby incorporated by reference.

BACKGROUND

Schools, adult care facilities, penal institutions, and other entitiesthat deal in significant population management encounter enormousdifficulty observing health and physical characteristics of individualsunder their supervision. Illness and injury are persistent threats tothe general health and safety of groups of individuals when they spendsubstantial time in close proximity. Detecting illnesses or injury earlycan be difficult in the initial phases, particularly for children whomay not recognize their own symptoms. The longer a sick individualremains in the population, the greater the risk of infecting others.

SUMMARY

The various embodiments provide a system for wireless monitoring ofindividuals within a defined space that can detect an exceptioncondition and respond with configurable, graduated measures. The systemmay employ wireless biometric sensor devices to relay real-time dataabout individuals' physical states to a server. Current biometricmeasurements may be evaluated for anomalies using biometric models,which may be derived from previous sensor measurements, externalvariables, and configured parameters. If the server discerns theexistence of an anomalous condition, the system may transmit dynamicalert notifications to caregivers that correspond to the urgency andnature of the situation. Thus, health and safety concerns may beautomatically detected and addressed without human interaction or asignificant number of false alarms.

The various embodiments include methods, which may be implemented aserver as part of a system, monitoring a population of individuals forhealth and safety, including generating a biometric model of nominal andthreshold biometric parameters for each individual of the populationbased on biometric sensor data obtained by one or more biometric sensorsand transmitted wirelessly from a plurality of wireless biometric sensordevices which are connected to the sensors and carried by theindividuals of the population.

The biometric model for each individual may be generated by the serveranalyzing received biometric sensor data for each individual over aperiod of time to calculate average and threshold values for eachbiometric parameter as a function of scheduled activities. Thisbiometric model may be used to monitor individuals in a system in whichthe plurality of wireless biometric sensor devices transmit currentbiometric sensor data to a server. The biometric parameters measured bythe biometric sensors may include one or more of temperature,acceleration, pulse rate, blood pressure, blood oxygen level, bloodsugar level, pH of skin, and presence of perspiration. The system orserver may process the biometric data by associating the currentbiometric sensor data received from each of the plurality of mobiledevices with a respective individual, evaluating the current biometricsensor data for the individual using the biometric model for thatindividual, determining whether an exception condition exists by notingwhen the current biometric sensor data is outside of a nominal range ofat least one biometric parameter for the individual. The server may alsoupdate the biometric model for the individual based on the currentbiometric sensor data. The biometric model for each individual may bedetermined by the server analyzing received biometric sensor data foreach individual over a period of time to calculate average and thresholdvalues for each biometric parameter as a function of temporalconditions. The server may maintain the biometric model evaluate thecurrent biometric sensor data for the individual using the biometricmodel for that individual by comparing the current biometric sensor datafor the individual to nominal ranges of the biometric parameters forthat individual in similar temporal conditions, scheduled activities,temperatures, particular locations, and atmospheric conditions. Thebiometric model may be continuously updated, such as by identifyingdependencies between various biometric parameters over time, adjustingnominal and threshold values to represent the current biometric sensordata, learning from operator feedback (e.g., feedback that an individualreally was or was not sick or injured at particular time).

The server may transmit an alert notification in response to determiningthe exception condition. The type of alert notification generated andthe recipients of the alert may be determined based on the exceptioncondition. Alert notifications may be sent as electronic, symbolic, ortelephonic communications to one or more than one recipient. The nature,recipients and level of alert may be escalated based changes in theexception condition and/or failure of a recipient to respond. Further,the server may learn from past alert transmission regarding recipientsand types of communications that are most effective for particularindividuals, and apply this learning in further alerts.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and constitutepart of this specification, illustrate exemplary embodiments of theinvention, and together with the general description given above and thedetailed description given below, serve to explain the features of theinvention.

FIG. 1 is a communication system block diagram of a network suitable foruse with the various embodiments.

FIG. 2 is a system block diagram of a communication system suitable foruse with the various embodiments.

FIG. 3 is a process flow diagram illustrating an embodiment method forassessing and indicating the existence of biometric anomalies.

FIG. 4 is a process flow diagram illustrating an embodiment method forevaluating biometric characteristics of individuals.

FIG. 5 is a process flow diagram illustrating an embodiment method forconducting adjusting notifications.

FIG. 6 is a process flow diagram illustrating an embodiment method forconducting adjusting notifications using previous experiences.

FIG. 7 is a component block diagram of a server computing devicesuitable for use with the various embodiments.

FIG. 8 is a component diagram of a wireless biometric sensor devicesuitable for use with the various embodiments.

DETAILED DESCRIPTION

The various embodiments will be described in detail with reference tothe accompanying drawings. Wherever possible, the same reference numberswill be used throughout the drawings to refer to the same or like parts.References made to particular examples and implementations are forillustrative purposes, and are not intended to limit the scope of theinvention or the claims.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any implementation described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other implementations.

The terms “wireless mobile device” and “wireless device” are usedinterchangeably herein to refer to any one or all of cellulartelephones, smart phones, personal or mobile multi-media players,personal data assistants (PDA's), laptop computers, tablet computers,smart books, palm-top computers, wireless electronic mail receivers,multimedia Internet enabled cellular telephones, wireless gamingcontrollers, and similar personal electronic devices which include aprogrammable processor and memory and circuitry for sending and/orreceiving voice and data calls, sending and/or receiving messages (e.g.,short message service (SMS) text messages, e-mails, etc.).

The term “wireless biometric sensor device” is used herein to refer to adevice that may be worn or carried by a user, equipped with at least onebiometric sensor, and configured to interact with a wirelesscommunication system. In an embodiment, a wireless biometric sensordevice may be configured to be worn by a user around the user's wrist ina manner similar to that of an ID tag or watch. In alternativeembodiments, a wireless biometric sensor device may be in the form of abadge, tag, bracelet, patch, belt buckle, or medallion, to name but afew examples.

The various embodiments provide a system for intelligently tracking anobserved population using wireless biometric sensor devices, evaluatingvarious biometric measurements against stored information relevant to anindividual, and issuing alert communications when a sensed conditionindicates a likelihood of illness or injury. Examples of observedindividuals wearing wireless biometric sensor devices include childrenattending a daycare program, students in a school, and patients withinan assisted living community. The embodiments may include wirelessbiometric sensor devices equipped with wireless communication (e.g.,Bluetooth® radios) and biometric sensing capabilities which, whenaffixed to an observed individual, transmit biometric sensor data viawireless signals to receiver devices, which relay the biometric sensordata to a central computing unit, such as a server. The computing unitor server may evaluate biometric data received from an individual usinga biometric model. The computing unit may generate and update thebiometric model by analyzing the combination of biometric parameterinformation collected from the observed individual over time, configuredparameters, and external variables and information. When receivedbiometric data exceeds nominal or threshold values, the computing unitmay transmit an alert to notify pre-designated individuals.

The various embodiments may be implemented on a variety of computingunits, such as a server, a personal computer, a work station, and anetwork controller when configured with processor executableinstructions to perform the operations of the embodiment methodsdescribed herein. For ease of description, the various embodiments aredescribed below referring to a server as performing the operations ofthe computing unit. However, the reference to servers is forillustration purposes and is not intended to limit the application to aparticular type of computing unit, network architecture orimplementation unless specifically recited in the claims.

Current biometric sensor data received from the individual's wirelessbiometric sensor device may be evaluated against the biometric model bycomparing the data to nominal and/or threshold values for similar times,activities, and/or locations. In an embodiment, events and environmentalvariables not unique to the observed individual, such as an activitiesschedule or outside weather conditions, may be incorporated into thebiometric model and data evaluations. The server may continually updatethe biometric model as the system receives biometric data, and theserver may update stored values and determine logical dependencies ofbiometric measurements to other variables, including other biometricparameters or attributes. Using biometric models to analyze currentbiometric sensor data of an observed individual, the server maydetermine whether an exception exists that should be brought to theattention of overseeing parties, such as system operators, guardians, orother authorities. Examples of exception conditions may include abnormalsensor measurements (e.g., high body temperature and/or pulse rate),inoperable received parameters, or missing data from observedindividuals. When an exception condition is recognized, the server maygenerate and transmit an alert notification to a pre-designatedindividual or individuals.

In various embodiments, the server may be configured to provide dynamicnotifications of determined exception conditions. When the serverconcludes that current biometric sensor data received for an observedindividual indicates the existence of an exception condition, the servermay send an alert notification to wireless devices of overseeingparties, so they can investigate the observed individual. Examples ofalert notifications may include telephonic, symbolic or electroniccommunications (e.g., SMS text messages, emails, or pages). Variousexception conditions may have differing levels of urgency, so the servermay issue alerts that depend on or are appropriate for the detectedcondition. For example, a determined exception indicating the potentialabduction of an observed child may require a high level alertnotification that is issued to many recipients (e.g., overseeingparties) using many forms of communication, while a mild fever may onlyrequire a message to a teacher, daycare attendant, or nurse.

An embodiment may enable response communications from recipients whoreceive alert notifications, and a server may adjust (e.g., escalate)the characteristics of alert notifications based on those responses. Forexample, if the server fails to receive a response from an alertnotification recipient regarding a low-level alert notification, theserver may send a medium-level alert notification to the same recipient.In another embodiment, the server may adjust the characteristics ofalert notifications or determined exceptions based on subsequentreceived biometric sensor data. For example, if the server receives dataindicating troubling biometric measurement changes in a persistingexception condition (e.g., a rising fever), the server may adjust thealert level and transmit a higher level alert notification regarding theexception. In an embodiment, the server may store and evaluate previousnotification experiences to adjust the characteristics of an alertnotification (e.g., the types of communications sent and the addressesused to send the communications). For example, the server may determinethat, based on previous notifications, an alert notification recipientmay respond quicker to an SMS text message alert notification than anemail communication.

FIG. 1 illustrates a network system 100 suitable for use with thevarious embodiments. The network system 100 may include multipledevices, such as wireless biometric sensor devices 102 and wirelessreceiver devices 103. The wireless biometric sensor devices 102 and thewireless receiver devices 103 may exchange data via wireless signals ordata links 106. As an example, the wireless data links 106 between thewireless biometric sensor devices 102 and the wireless receiver devices103 may be Bluetooth® or other similar short-wavelength radiotransmissions. As another example, the wireless data links 106 betweenthe wireless biometric sensor devices 102 and the wireless receiverdevices 103 may be WiFi transmissions, where the wireless receiverdevices 103 may act as WiFi network access points (e.g., WiFi routers).Wireless receiver devices 103 may be within or outside of a structure107. A wireless biometric sensor device 102 may transmit wirelesssignals 106 that may be received by the closest wireless receiver device103 within the system 100. In another embodiment, wireless biometricsensor devices 102 may communicate with other wireless biometric sensordevices 102 which may relay transmissions to wireless receiver devices103 in the system 100.

The wireless receiver devices 103 may include a wireless receivercircuit, such as a Bluetooth® transceiver, and a network interfaceconfigured to relay sensor data received via wireless signals to aserver 105 via a network, such as the Internet 108 or a local areanetwork. The server 105 and wireless receiver devices 103 may exchangedata bi-directionally, which may enable the server 105 to employ thewireless receiver devices 103 to transmit wireless data signals to thewireless biometric sensor devices 102. Through the connection to theInternet 108, the server 105 may also exchange data with externaldevices capable of interacting with the Internet 108, such as a smartphone 109, a laptop 110, other servers, and a cloud data storage device111. In this manner, communications (e.g., SMS text messages, e-mails,etc.) may be exchanged between the server 105 and otherInternet-connected devices by methods well known in the art.

Wireless biometric sensor devices 102 may transmit data at pre-defined,regular intervals. For example, a wireless biometric sensor device 102may prepare and send wireless transmissions every few seconds. Theserver 105 may also periodically transmit, through the wireless receiverdevices 103, requests for wireless biometric sensor devices 102 totransmit data. For example, every few seconds, a scheduling applicationrunning on the server 105 may send requests to all wireless biometricsensor devices 102 receiving the wireless transmissions to respond withcurrent measurement data communications.

In an embodiment, the system 100 may employ transmission schedulingmethods to minimize wireless transmission collisions amongst thewireless biometric sensor devices 102 and wireless receiver devices 103.If numerous wireless biometric sensor devices 102 transmit datasimultaneously, the resulting interference might cause incomplete orcorrupted information due to radio signals arriving at the wirelessreceiver devices 103 simultaneously. A system's 100 transmissionscheduling methods may involve assigning particular times (e.g., a timewithin each minute) when particular wireless biometric sensor devices102 may exclusively transmit data to wireless receiver devices 103. Forexample, a particular wireless biometric sensor device 102 may beassigned a certain range of seconds within each hour to transmit towireless receiver devices 103, during which all other wireless biometricsensor devices 102 may not transmit to the wireless receiver devices103.

A wireless biometric sensor device 102 may transmit data messagesrepresenting biometric information about the individual using and/orwearing the wireless biometric sensor device 102. Biometric informationmay be measurements take from sensors located within the wirelessbiometric sensor device 102. Examples of such measurements may includebody temperature, pulse rate, and acceleration (i.e., body motion). Thedata messages may include identification information about the wirelessbiometric sensor device 102, such as a unique ID number or code. Thewireless biometric sensor device 102 may encrypt or make the datamessages otherwise obscured by data security abstractions, which theserver 105 may reverse through decryption techniques to make theinformation useable. In another embodiment, the wireless receiverdevices 103 may also process data messages with encryption and/ordecryption techniques.

As an illustrative example, a wireless biometric sensor device 102having a unique identification code may include pulse rate and bodytemperature sensors, both being in contact with an observed individual'sanatomy sufficient to produce measurements. The wireless biometricsensor device 102 may, via the sensors, determine the body temperatureand pulse rate of the observed individual. The wireless biometric sensordevice 102 may concatenate the temperature value, pulse rate value, anddevice identification code in a manner that may be parsed and understoodby an associated server 105. The wireless biometric sensor device 102may also execute a routine applying an encoding algorithm to theconcatenated data, producing a data message that may be transmitted tothe wireless receiver devices 103 and relayed to the server 105. Whenthe server 105 receives the data message, the server 105 may applydecryption and parsing routines to the data message in order to producediscrete information segments representing the pulse rate, bodytemperature, and the identification code of the wireless biometricsensor device 102.

In an embodiment, wireless biometric sensor devices 102 may provideinformation about their location. Wireless receiver devices 103 maydetermine the location of wireless biometric sensor devices 102 throughthe use of ranging calculations based on data signal exchanges betweenthe wireless receiver devices 103 and the wireless biometric sensordevices 102. In another embodiment, wireless biometric sensor devices102 may include global positioning system (GPS) chips and report GPScoordinates via the wireless data links 106.

Data transmitted to the server 105 may include other non-biometricinformation, such as atmospheric conditions or physical location of anobserved individual, which may be used in the evaluation of biometric,safety, or health status. For example, if an observed individual wearinga wireless biometric sensor device 102 passes a Wi-Fi hotspot (or localarea network) unaffiliated with the network system 100, the wirelessbiometric sensor device 102 may transmit identifying characteristics ofthe hotspot to the server 105. As another example, if a wirelessbiometric sensor device 102 is equipped with a chip capable of cellularnetwork communications (e.g., 4G LTE), the wireless biometric sensordevice 102 may transmit the current cellular network data signalstrength to the server 105. The server 105 may use such non-biometricinformation in combination with other data to extrapolate importantinformation regarding the observed individual. For example, the server105 may combine a frigid local atmospheric temperature measurementreceived from a child with a hot atmospheric temperature measurementreceived from an Internet weather report and determine that the childmay be trapped in a freezer unit.

If wireless routing devices, such as transceiver devices 103, areemployed in the delivery or direction of data to a server 105,additional information may be appended to the data originally sent bywireless biometric sensor devices 102. For example, an observedindividual's wireless biometric sensor device 102 may transmit a datamessage that includes only its unique device identifier number and thecurrent measurement of a body temperature sensor. The wireless receiverdevice 103 closest to the wireless biometric sensor device 102 mayreceive this data message and add its own identifying code onto the datamessage. Providing an identifier of the wireless receiver device 103 toa data message may enable the server 105 to determine the approximatelocation of the observed individual based on the known location of thatwireless receiver device 103 and the communication range of wirelessbiometric sensor devices 102. Alternatively, a wireless receiver device103 may append to transmitted data messages data transfer statistics,such as elapsed time between original transmissions to wirelessbiometric sensor devices 102 and their transmission responses to thewireless receiver devices 103. Such additional data may be used by theserver 105 to troubleshoot network latency issues or even diagnosewireless biometric sensor device 102 functionality deficiencies.

The server 105 may store data it receives or generates within anelectronically-stored database. If data relates to a particular observedindividual, then the server 105 may store the data in the database suchthat it is functionally-connected to the observed individual (i.e.,related database records share a unique ID key). A query may besubmitted to database management software running on the server 105,from which data about the observed individual may be returned for use bythe server 105. For example, a server 105 may access all the bodytemperature values stored after transmissions by a child's assignedwireless biometric sensor device 102.

Information within the database may be in the form of data records,which may include numeric and text data, and may be divided intonumerous functionally descriptive categories. For example, a server's105 broad query of the database for records pertaining to an observedindividual ID, may return one thousand records, each consisting ofnumeric values for the data attributes ‘time’, ‘date’, and‘body_temperature’ and a text value for attribute ‘location.’ As anotherexample, a query of the database for records pertaining to that same IDbut limited to the data attribute ‘body_temperature’ may return onethousand records consisting of only ‘body_temperature’ numeric values.

The information stored within the database may be a comprehensivearchive of data that correspond to discrete measurements and temporalconditions (e.g., time of day, day of week, etc.). As wireless biometricsensor devices 102 transmit data to the server 105, each measurement orindividual piece of information may be stored as tracked againsttemporal conditions (e.g., day, month, year, and time of day). Forexample, the database may possess countless records for a child's bodytemperature, one value stored for each transmission from his wirelessbiometric sensor device 102. The server 105 may access each recordindividually, and may retrieve copious records for particular timeperiods. In another embodiment, the database may not store eachindividual measurement, but instead maintain summary values in adatabase which evolves over time as summary values change as subsequentmeasurements are received by the server 105, such as a moving average.For example, instead of storing each temperature measurement in thedatabase, the server 105 may only update and store crucial information,such as the average temperature, statistical bounds about the averagetemperature, and the number of total measurements received. The server105 may save database storage space and computational costs by storingonly summary information and discard individual measurements after themeasurements have been evaluated against the model and the database hasbeen updated.

The server 105 may store any information retrieved from the database inrandom access memory for immediate use, or alternatively, may store suchinformation in local access storage, such as a non-volatile hard drive.To conserve space in local access storage, decrease computational costs,or minimize storage access costs, the server 105 may delimit its accessto database records by only retrieving relevant subsets of informationabout the observed individual. For example, although the databasecontains stored information about a child's body temperature, pulserate, perspiration, skin pH, and motion activity, the server 105 mayonly request data regarding the child's body temperature.

FIG. 2. illustrates a system 200 suitable for use with the variousembodiments. The system 200 may include a wireless biometric sensordevice 102 worn by an individual in the observed population and awireless device 210 accessible or carried by an individual acting in anoverseeing capacity. The system 200 also may include a server, such as aserver 205. In another embodiment, the server may be portable, such as asmart phone carried by an overseeing party. The wireless biometricsensor device 102 and a wireless receiver 204 associated with the server205 may transmit data via a wireless data link 203. The wireless device210 and the wireless receiver 204 may transmit data via another wirelessdata link 211, such as WiFi, or via an external communication network215 (e.g., a cellular network). As an example, the wireless data link203 between the wireless biometric sensor device 102 and the wirelessreceiver 204 may be Bluetooth®, Zigbee®, or other similar relativelyshort-wavelength radio receiver or transceiver.

Data sent to the server 205 by the transceiver 204 may be analyzed in aninference processing unit 206, which may be a software moduleimplemented in the server 205. In another embodiment, the inferenceprocessing unit 206 may be a dedicated processing device within orcoupled to the server 205. The inference processing unit may exchangedata with the transceiver 204 in a bidirectional data flow. As anexample, the wireless receiver 204 may process incoming data viawireless data link 203 from wireless biometric sensor device 102 anddeliver that information to the inference processing unit 206 to bestored in memory and used to evaluate the status of an observedindividual. As a further example, the inference processing unit 206 maydirect an alert notification to the wireless receiver 204 and/or anexternal communication network 215 for wireless transmission to thewireless device 210 to alert an overseeing party.

Data received, requested and created by the server 205 may be stored andorganized in the knowledge database 207 by the inference processing unit206. The knowledge database 207 may be stored within high-capacitystorage connected to the server 205. The knowledge database 207 may havethe structure of a relational database and accept data originating fromwireless biometric sensor device 102 measurements. The server 205 and/orthe inference processing unit 206 may retrieve data from the knowledgedatabase 207 for use in evaluating biometric information received fromobserved individuals. Data within the knowledge database 207 may beupdated, replaced or removed based on relevant measurements,calculations and analytical determinations of the server 205 and/or theinference processing unit 206. The knowledge database 207 may be storedwithin local computer storage, such as in non-volatile hard drives. Inanother embodiment, the knowledge database 207 may be partially or fullystored within remote computer storage (e.g., “cloud” storage) that maybe accessed through various Internet connections. When the knowledgedatabase 207 is maintained in remote storage, multiple computing devicesmay access data pertaining to observed individuals, enabling monitoringof observed individuals at multiple installations.

In an embodiment, individuals within an observed population may beassociated with or assigned to particular wireless biometric sensordevices 102. For example, while at daycare, a child may only ever wear aparticular wireless biometric sensor device 102, which may be identifiedwith a unique identification code. The identity of a wireless biometricsensor device 102 may be synonymous with the identity of an observedindividual, and such associating relationships may be electronicallyrecorded and changed within a data table. In an embodiment, the identityof the wireless biometric sensor device 102 may serve as a databasequery key for use while retrieving and/or updating information about anobserved individual from the knowledge database 207, as described above.

FIG. 3 illustrates an embodiment method 300 for a computing unit (e.g.,a server) determining and acting upon exceptions based on the analysisof gathered biometric data. Over a period of time, a server implementingthe embodiment method 300 may amass biometric sensor data of individualsin an observed population and derive biometric models which indicatenormal biometric data values and patterns for the individuals. In anembodiment, the server may compare such biometric models against currentbiometric measurements of the individuals to recognize any anomalous, orexception, conditions. If an exception condition is acknowledged by theserver as present or probable, the server may send notifications tooverseeing parties. Any biometric measurements received may be storedand/or applied to update the biometric models for use in futuredeterminations.

In block 302, a server may generate biometric models for individuals inan observed population based on biometric data gathered over a period oftime. In an embodiment, the system, such as described above in FIG. 1,may operate in an initial state of data gathering for a period of time.During this initial data gathering period, the server may receive asignificant data set of biometric sensor measurements from which it maygenerate a biometric model for each of the individuals in the observedpopulation. Multiple measurements of many biometrics parameters (e.g.,temperature, pulse rate, etc.) for each individual may be transmitted bywireless biometric sensor devices to the server as data messages, whichmay include both numeric and text content.

In block 302, the server may develop a biometric model for each observedindividual by analyzing data collected from the system and representedwithin a database. The biometric model may be based on that individual'sunique set of biometric data received from his assigned wirelessbiometric sensor device and configured as a benchmarking tool to be usedby the server when evaluating current biometric sensor data. Thebiometric model, for example, may represent the nominal and thresholdvalues of biometric attributes of the observed individual. Nominal andthreshold ranges may describe the typical values for biometricattributes of the observed individual and may be used to determine ifbiometric measurements are anomalous (i.e., exceptions). As anotherexample, a threshold within a biometric model for a child may be set forbody temperature measurements that are a degree above or below theaverage body temperature for a child. Nominal and threshold ranges maybe defined as a function of many factors, such as temporal conditions(e.g., time of day, day of week, day of year) and/or physical locations.For example, the nominal and threshold values for body temperature maybe calculated as a function of the time of day and day of the week sincea child's body temperature may change throughout the day naturally anddepending on activities. In another embodiment, threshold values may besupplemented by other parameters, such as user-defined system variablesstored electronically on the server. For example, a configuration filemay set a threshold at two standard deviations from the mean biometricattribute value, with any measurement falling outside of this range tobe determined an exception. In such an implementation, a reportedtemperature for a child may be recognized as an exception by the serverif it is outside of two standard deviations from the calculated meantemperature for the child.

In block 302, when determining the nominal and threshold values of thebiometric model of an observed individual, the server may also developtypical statistical metrics for each biometric attribute of thatindividual. Statistical metrics may include mean and standard deviationvalues for a biometric attribute. For example, the server may determinethat the average and standard deviation for reported body temperaturesof a child. In an embodiment, the server may store summary values of thebiometric attributes, such as running averages. Statistics may also bederived using subset samples of relevant data stored in databases.

The statistical metrics may be refined by the server for specific timeor date ranges to further develop the biometric model of an observedindividual. For example, the server may determine the mean bodytemperature of a child across all times reported. However, the servermay also determine the mean temperature for measurements taken duringparticular temporal conditions (e.g., time of day, etc.). The server maycalculate a confidence assessment of any statistical determinationsbased on factors such as size of data set and variation of recordingconditions.

The server may adjust statistical assessments described in biometricmodels through trend analysis. If biometric measurement values fallwithin the fringe of nominal parameters, the server may extrapolate anytrending toward an exception. For example, if a body temperaturemeasurement for a child is within a small amount of the exception range,the server may query stored, previous measurements for the child'stemperature to discern if the child has been experiencing an increasingtemperature throughout the recent past. When observing such fringemeasurements, the server may determine a higher probability of anexisting illness by failing to find similar progressions in the storeddata. In another example, accelerometer measurements, showing a highamount of movement (or acceleration) of an observed child, may fallwithin average values for that child throughout the day. However,through an examination of measurements represented by the child'sbiometric model, the server may discern the seemingly normalaccelerometer measurement to be a strong basis for an exceptiondetermination, as the activity may be occurring when the child hashistorically been relatively motionless.

Statistical analysis and trending may include data from the entirepopulation as well as from the observed individual. In an instance whereshallow data sets exist within the database for an observed individual,the server may reinforce statistical assumptions by supplementing normaldata from other observed individuals in similar conditions. For example,during a child's first hour of daycare, the server may evaluate hismeasured accelerometer motion data against the average values from hisentire class.

An observed individual's biometric model may be a complex data structurethat relates data points to conditions that may affect the individual'sbiometric measurements. In an embodiment, a biometric model datastructure may juxtapose normalized data ranges of measured biometricattributes, computed from information within previous individual datamessages, to other factors, such as time of day, day of week, localtemperature, etc. For example, the averages of a child's observedbiometric attribute measurements, such as body temperature or pulserate, may be paired with time periods corresponding to the measurements.Such a data structure may be multi-dimensional, including sets ofbiometric measurement ranges against N functional factors at once. Forexample, average value ranges for biometric attribute measurements maybe in context of one factor, such as time of day, and an additionalfactor, such as location. Alternatively, in an embodiment the server maygenerate a biometric model that is the form of an algorithm in whichmeasurements may be sequentially evaluated using factors determined fromprevious observations. For example, an algorithmic biometric model mayevaluate a current measurement to output a probability that theindividual is ill or injured, and potentially an indicator of thepossible type of illness or injury.

As the server receives more observed biometric data, the server mayrefine the biometric model to provide better representations of nominalcharacteristics and exception-indicating threshold parameters. Withlarger data sets of biometric measurement values, computed ranges usedwith biometric models may become more accurate in recognizing illness orinjury.

In an embodiment, the system may employ machine learning intelligence togenerate and improve the biometric models based on received biometricdata. The biometric model may identify connections between biometricattributes and other factors within the system that exhibit some amountof dependency. For example, new measurements for particular biometricattributes may not correspond to the biometric model's currentapproximation of nominal values. However, if information from thesystem, such as a user input, portrays the new measurements as normal,the biometric model may include a new variable or weight currentbiometric data for use in future evaluations of the biometric attribute.The server may draw inferences regarding causal relationships orconnections between attributes and seemingly unrelated data. Based onpast experiences, the biometric model may develop new dimensions withina biometric model data structure or new branches of evaluation within abiometric model algorithm.

Operations involved in block 302 may continue indefinitely or may beactivated for a particular period of activity. For example, a daycaredeploying an embodiment of the system may require a long period of datacollection to ensure the server generates more accurate biometricmodels. The daycare may execute a data collection period of a month soas to experience a high amount of data as transmitted by childrenwearing wireless biometric sensor devices. Alternatively, the operationsin block 302 may involve loading a default biometric model. For example,if an embodiment is implemented at a daycare, the system may usenational averages for children of comparable age and socioeconomiccharacteristics as the initial default biometric model. Biometric modelsmay be refined over time as discussed below with respect to block 310.

Continuing with FIG. 3, in block 304, the server may receive currentbiometric sensor data from wireless biometric sensor devices carriedand/or worn by individuals in the observed population. As describedabove, the wireless biometric sensor devices may send data messages tothe server containing information derived from biometric sensors, aswell as other information, such as location coordinates. The server mayreceive, parse, decrypt, and associate the data with respective observedindividuals.

In block 306, the server may employ generated biometric models toevaluate current biometric sensor data. As it receives current biometricmeasurement data from a wireless biometric sensor device, the server maycompare that data to the biometric model of the respective individual todetermine whether the current data represent anomalous conditions (i.e.,exceptions). Exception conditions may be those which suggest physicaldistress, illness, or abduction of the observed individual. Anembodiment of this exception existence evaluation is discussed atgreater detail below with reference to FIG. 4.

In block 308, the server may transmit an alert notification based on theexception evaluations. If a comparison of the received current biometricsensor data to biometric models suggests the existence of an exception,the server may communicate with overseeing parties, via alertnotification, to prompt them to investigate or remedy the exception.Embodiments of the alert notification creation and delivery operationsare discussed in more detail below with reference to FIGS. 5 and 6.

Alert notifications that may be transmitted in block 308 may be ofnumerous forms, such as SMS texts, emails, and telephone calls withmachine-generated or prerecorded verbal messages. Alert notificationsmay have different associated alert notification recipients ordestinations for communications. Alert notification recipients and theircontact information may be stored as address books, which may bedescribed in electronic files accessible by routines running on theserver. Examples of alert notification recipients may be overseeingindividuals (e.g., teachers) or parents of observed individuals.

In an embodiment, alert notifications may be transmitted to theappropriate alert notification recipients by the server as datamessages. The data message may be formatted by the server for deliveryvia email, text SMS message over a cellular network connection, or viaother wireless data transmission (e.g., short range radio signal). In anembodiment, data messages may be sent via wireless transmissions fromthe server to wireless devices employed by alert notificationrecipients.

In an embodiment, alert notifications may include identifyinginformation regarding the observed individual (e.g., wireless biometricsensor device 102 identification number) and his physical location(e.g., “outside near router #4”). The server may employ a data lookuptable to find the observed individual's name and biographical details toinsert into the alert notification. The alert notifications may alsoinclude the biometric attribute(s) which the server determined ascausing the exception. In addition, the server may procedurally generateprose that gives intuitive descriptions of the determined exception,such as possible diagnosis and/or a summary of symptoms. For example,after determining an exception regarding a child's current hightemperature, the server may generate the text “Child A has a hightemperature and may be getting a cold.” In another embodiment, the alertnotification may include instructions regarding how the determinedexception may be remedied. For example, based on the preceding examplescenario, the generated text may also include “Please have Child A takento the nurse for immediate observation.” The alert notification may befurther characterized by an indicator which signals whether the alert isof low, medium, or high importance. Other descriptive information may beappended to the alert notification as well, such as a unique alertnotification identity code that may be used by the server in linkingresponses and follow up actions to alert notifications.

Returning to FIG. 3, in block 310, the server may update the biometricmodels using received data. The server may use the current biometricsensor data to modify the current biometric model (e.g., updatingrunning averages), and store the updated biometric models for use inevaluating subsequent biometric data sets. Alternately or periodically,the server may use all received biometric sensor data, including thecurrent data, to regenerate each individual's biometric model(essentially repeating the operations of block 302 including recentlyreceived biometric data). In an embodiment, block 310 operations mayinclude adjusting nominal and threshold values used to recognizeexception conditions. Such updates may refine the statistical elementsof the biometric models as more biometric data are received, which mayenable future exception evaluations to be more accurate. The server maymake database updates by changing values within a locally storeddatabase (e.g., in a knowledge database 207 within the server), or bytransmitting data via Internet protocol communication to remote storagedevices (e.g., cloud data storage device 111). In another embodiment,updating of the biometric models and other database information mayoccur during any operation within method 300.

FIG. 4 illustrates an embodiment method 400 for recognizing exceptionconditions by analyzing current biometric sensor data. The server maycompare current biometric measurement data from an observed individualto the biometric model for that individual. In an embodiment, theinference processing unit may perform various distinct evaluations ofdata to ascertain the existence of exception conditions. Each evaluationmay weigh each biometric element differently in a calculation of anexception condition or probability, and such weighting schemes may bedefined within each individual's biometric model. Alternative orsupplementary weighting schemes may be defined in user configurationfiles. The final result of an exception calculation may be a floatingpoint numeric value that reflects a probability that the individual isill or injured, which are referred to generally as exception conditions.For example, the inference processing unit may calculate that thecurrent biometric measurement of a child's body temperature has a 25.5%probability of being an exception. In another embodiment, the exceptionevaluation may determine the existence of an exception as a binaryindicator, such as ‘yes’ or ‘no’.

In block 402, the inference processing unit may begin analyzing datareceived from an individual's wireless biometric sensor device. Forexample, the inference processing unit may determine whether the datamessage contains incomplete or erroneous information. In an embodiment,the inference processing unit may compare the expected types, formatsand ranges of measurement data (e.g., the server requests measurementsof body temperature and pulse rate) to the data actually received fromwireless biometric sensor devices. For example, the server may transmithourly commands to all wireless biometric sensor devices within thenetwork to report, via data message, body temperature, location, andpulse rate measurements. Operations in block 402 may also detect dataerrors or corruption. When incomplete or grossly erroneous data arereceived, the inference processing unit may discard such data. Theserver and/or inference processing unit may also initiate a hardwaremaintenance request to inspect the wireless biometric sensor devicewhich produced the incomplete data message.

In block 404, the inference processing unit may begin analyzing gathereddata represented in a biometric model to determine anomalies, orexception conditions, in current biometric sensor measurements. Theinference processing unit may evaluate the current biometric sensor datausing expected deviations in values for biometric attributes representedin an individual's biometric model. In an embodiment, the inferenceprocessing unit may detect exception conditions if current biometricmeasurements do not coincide with the biometric model's threshold andnominal values for particular biometric attributes. For example, if thecurrent measurement of a child's temperature is outside of the thresholdrange for normal temperatures for the child, the inference processingunit may determine an exception exists. The inference processing unitmay also use statistical metrics related to the biometric model, as wellas trending analysis, to evaluate current biometric measurements forexception conditions based on the biometric model data. In anembodiment, the inference processing unit may use temporal conditions(e.g., time of day, day of week, etc) and physical location of theindividual to narrow the focus of the analysis of current biometricmeasurements against the biometric model.

In block 406 the inference processing unit may evaluate biometric dataand potential exception conditions against other information that mayhave indirect effects on the measurement data. If a biometric attributemeasurement is determined to be outside of the threshold value for anobserved individual in block 404, the inference processing unit mayevaluate whether there are variables that may be affecting the biometricmeasurement. Additional variables may be represented in the biometricmodel and may include, for example, location of the observed individual,atmospheric conditions (e.g., temperature, wind chill, precipitation),and scheduled activities (e.g., recess or nap time). If the inferenceprocessing unit determines such a variable is affecting an individual'sbiometric measurements, the inference processing unit may adjust thebiometric model analysis and reevaluate the finding of an exceptioncondition. As an example, an activities calendar, giving time, place,duration and nature of scheduled activities, may be electronicallystored on a server and accessible by the inference processing unit aspart of block 408. If a child's wireless biometric sensor device reportsa current body temperature higher than statistical norms for similartime periods, the inference processing unit may access the schedule todetermine whether the child is scheduled to be participating in aphysical activity which might affect the measurement. For example, theinference processing unit may temporarily increase the threshold forbody temperatures for the child during the time of the scheduledphysical activity.

In an embodiment in which the inference processing unit may communicatedirectly or indirectly with remote servers via the Internet, theinference processing unit may also gather pertinent information fromreal-time third party resources as part of the operations in block 406.For example, using Internet protocol communications, the inferenceprocessing unit may request and receive atmospheric temperature data fora relevant zip code from the National Weather Service website, and usethis information when evaluating an observed individual's heightenedbody temperature measurement.

In block 408, the inference processing unit may use previously confirmedor overruled exception conditions to analyze current biometricconditions. If any reported exceptions were confirmed or determinedpreviously, the inference processing unit may consider such informationin evaluating whether an exception condition exists. For example, if thecurrent accelerometer data for an observed individual is within therange of previous non-exception values for a particular time, theinference processing unit may not determine a high probability of anexception existing. However, if stored data indicates an exceptionoccurred with similar motion data and time period, the inferenceprocessing unit may modify its initial assessment to weigh biometricdata more narrowly. In an embodiment, the inference processing unit maycompare all current biometric attribute values to all known occurrencesof exceptions in order to determine a connection or relationship amongstvarious biometric attributes and other conditions based on previousdecisions made by overseeing parties. As an example, there may be norecorded exceptions for an individual with a certain combination ofvariable values (e.g., body temperature is high, body motion is active,and location is inside). However, the stored data from the database maydescribe that an exception was reported for the individual with only aslightly different combination of variables values (e.g., bodytemperature is high, body motion is active, and location is outside).The inference processing unit may determine the disjoint in the two setsof variable values and record a relationship or connection between thevariables. In an embodiment, the inference processing unit may alsoutilize predefined attribute relationship tables that are stored,accessible, and modifiable by the server.

In block 410 the results of operations in blocks 402-408 may be combinedor analyzed in unison to determine an exception condition exists. Thebiometric model for an individual and system settings may includeweighting factors that the inference processing unit may consider inexception existence evaluations. For example, based on userconfigurations, the inference processing unit may not consider howbiometric attributes affect one another if a biometric measurement iswithin one standard deviation of the mean values of all observedindividuals. In another embodiment, user configurations may place moreemphasis on non-biometric factors, such as class attendance rates andactivity schedules, which may indicate that an illness is being passedamong individuals in an observed population. For example, the inferenceprocessing unit may calculate a higher probability of an exceptionexisting when a child is exhibiting only a nominally high bodytemperature, but there is currently a high absence rate in theclassroom.

As described above, the exception assessment conclusion may berepresented by the inference processing unit as a probability orpercentage of likelihood that an exception exists. As an example, if achild has a very high reported current body temperature measurement, theinference processing unit may determine an exception existenceprobability of 90%.

FIG. 5 illustrates an embodiment method 500 for creating and managingalert notifications for biometric attribute exceptions. In determinationblock 502, the server may determine whether an alert notification actionis required based on exception existence evaluations, such as describedabove in respect to the embodiment in FIG. 4. The need to transmit analert notification may be determined by comparing a floating pointprobability (e.g., 33%), a binary indicator (e.g., 0 or 1), or any otherindicator of the existence of an exception (e.g., “strong probability”)to a notification criteria or threshold. In an embodiment, the servermay only determine an exception and continue with alert notificationactions if exception existence evaluations are represented asaffirmative binary values (i.e., ‘1’). If the server determines thatthere is no exception requiring further alert notification action (i.e.,determination block 502=“No”), the server may update the database andbiometric model based on the biometric sensor data, as described abovewith reference to block 310 in FIG. 3.

In another embodiment, the server may determine an exception indetermination block 502 and continue with alert notification actions ifexception existence evaluations represent a minimum level ofprobability. For example, the server may determine an exception andpursue alert notification actions for all biometric measurementsevaluated as having a 75% probability of exception. As another example,the server may determine an exception and execute alert notificationactions when there is any possibility of exception existence (e.g.,“some possibility” or 1% probability). Administrators of such anembodiment system may customize the level of certainty required beforeany exception alert notification may be transmitted in order to abatefalse alarms or unnecessary alert notifications. For example, a facilityemploying an embodiment system and having limited resources (i.e., fewoverseeing parties) may set a threshold level at a 75% probability of anexception existing before initiating an alert notification.

In an embodiment, the server may determine an exception using othervariables, such as the biometric attributes themselves, in addition tothe exception existence evaluation. The server may treat certainbiometric attributes as special cases requiring alert notificationaction despite a lower probability of exception existence. For example,the exception existence evaluation for a child's pulse rate measurementmay be determined as low probability by the server; however, the servermay send alerts when there is a possibility of an exception related topulse rate.

Returning to FIG. 5, in block 504, if the server determines that anexception alert notification should be issued (i.e., determination block502=“Yes”), the server may determine and execute actions to conductalert notifications. The server may establish an alert level thatdefines the intensity or severity of the conditions regarding thedetermined exception. In an embodiment, the alert level may directlycorrespond to exception existence evaluations. For example, a “high”probability of exception determined from an exception existenceevaluation may result in a ‘high’ alert level notification. Certainbiometric attributes relating to determined exceptions may producepredefined alert levels. In an embodiment, the server may categorize anydetermined exception regarding certain “high concern” biometricattributes as a higher alert level and “low concern” attributes as alower alert level. For example, if the determined exception regards aslightly elevated perspiration measurement and a corresponding highprobability of exception existence, the server may classify theexception as a “low” alert level. However, if the determined exceptionregards an extremely low pulse rate and a lower exception existenceprobability, it may be a “high” or “crucial” alert level. An embodimentmay enable the use of any number of alert levels. Additionally, alertlevels may be gradating and sequential in nature, with low alert levelsprogressing in intensity to higher level alerts.

In an embodiment, specific actions may be associated with alert levelsand alert action protocols may define how the server transmits alertnotifications regarding the determined exceptions. Alert actionprotocols may encompass a number of different alert levels, each withvarying associated actions, and may be stored in electronicconfiguration files on the server. In an embodiment, administrators ofthe system, such as school principals or healthcare providers, maydefine alert action protocols using simple programming logic. As anexample: an administrator may define an alert action protocol using thecode “If alert is LOW, then text J. SMITH. If alert is HIGH, call R.JONES.”

In determination block 506, the server may execute a listening (orwaiting) routine that awaits a response to a transmitted alertnotification. The alert response listening routine may run concurrentlywith the normal functions of the server, persisting until the serverdetects a terminal response to the associated alert notification. In anembodiment, the alert response listening routine may persist for adefined duration, as may be indicated in an alert action protocol orother configuration file stored in the server. For example,administrators of the system may indicate that responses to all lowlevel alert notifications may be terminal if accepted by the serverwithin a day of its original transmission. If the server detects aterminal response to the alert notification (i.e., determination block506=“Yes”), then the server may update the database information withcurrent biometric attribute measurements and any relevant informationcontained within the alert response (e.g., information categorizing theexception as a false alarm). An embodiment of the update operations isdescribed above with reference to block 310 in FIG. 3.

In an embodiment, the server may maintain a data table of outstandingalert notifications which may be associated with active notificationresponse listening routines. Outstanding alert notifications maydirectly correspond with a particular determined exception and maypersist as outstanding until the server discontinues the listeningroutine associated with the particular alert notification. The servermay discontinue such listening routines due to the occurrence of severalevents, such as a lapse of a particular time period or the receipt of aterminal response (i.e., determination block 506=“Yes”). When an alertnotification ceases to be outstanding, the server may remove it from thedata table.

The server may use the outstanding alert notification data table whendetermining whether to transmit alert notifications. In an embodiment,the server may compare information from exception existence evaluations,such as described above with reference to block 306 in FIG. 3, with theoutstanding alert notification data table in order to avoid executingredundant or unnecessary alert notifications. For example, the servermay not acknowledge a new determined exception, and therefore notexecute a new alert notification, if there is already a persisting alertnotification represented in the outstanding alert notification datatable that regards the same basis for the exception, including the sameobserved individual. This comparison by the server may preclude thetransmission of redundant alert notifications.

In another embodiment, the server may query the outstanding alertnotification data table and adjust alert notification characteristicsdue to subsequent biometric measurements related to pre-existingdetermined exceptions. When the server determines that receivedbiometric sensor data corresponds to a pre-existing determined exceptionconnected to an outstanding alert notification represented in the datatable, the server may determine that the biometric sensor data is newinformation of an ongoing exception condition. This may occur whenexception conditions persist through multiple cycles of datatransmissions from the wireless biometric sensor device worn by aparticular observed individual. For example, a determined exception andalert notification may exist regarding a child's elevated temperaturewhen the server evaluates more recent biometric sensor data of showingan even higher temperature for the child. The server may interpret themore recent biometric sensor data as an update to the pre-existingdetermined exception and may execute a new notification based on thecomparison of the characteristics of both conditions. For example, theremay be a low-level, outstanding alert regarding a slightly elevated bodytemperature for a child in the outstanding alert notification datatable. The server may subsequently evaluate biometric sensor dataregarding an increased body temperature measurement for the child.Comparing the outstanding alert notification and correspondingdetermined exception with the subsequent biometric information, theserver may cause an alert level escalation and transmit a new alertnotification of heightened intensity or severity.

In an embodiment, the server may define responses as receivedcommunications that contain pairing information for an outstanding alertnotification. For example, if the server receives an electronic textcommunication regarding a determined exception having a unique code, theserver may deem the communication a response to that exception'soutstanding alert notification. A terminal response may be a responsereceived by the server that indicates that the server may discontinuethe listening routine associated with a particular alert notification.Using the immediately preceding example, the server may interpret aresponse regarding the determined exception's outstanding alertnotification as terminal if the response indicates that the exceptionhas been resolved. In another embodiment, all responses received by theserver may be terminal responses for their respective alertnotifications.

Alert notification responses may be in the form of direct input to theserver (e.g., a mouse click or keyboard entry command), data messagestransmitted from an alert notification recipient, or any otherelectronic communication sent by an alert notification recipient andreceived by the server. For example, an alert notification recipient,receiving an alert notification regarding a determined exception on hissmart phone, may click a graphical user interface button shown on thephone's display unit which initiates a wireless data messagetransmission to the server and which the server may interpret as aresponse regarding the exception. Alert notification responses maycontain information which may function as commands to the server. In anembodiment, responses may be sent by recipients which may direct theserver to continue or discontinue a particular response listeningroutine, validate a determined exception, change an alert level, modifyan alert notification action for a determined exception, or updateinformation accessible by the server, such as data stored in a database.For example, the recipient described in the above example may click a‘Disregard’ button on his smart phone after having received the alertnotification, causing the smart phone to send a data message to theserver, which interprets the data message as a command to discontinuethe listening routine corresponding to the exception. As anotherexample, the recipient may instead click a button ‘Escalate’ in responseto an alert notification, which may be interpreted by the server as acommand to increase the alert level of the exception.

Returning to FIG. 5, in determination block 508, when the server doesnot detect a terminal response (i.e., determination block 506=“No”), theserver may decide whether to continue the alert notification process byconducting further alert notification actions. The server may base thisdecision on factors including non-terminal alert notification responseinformation (e.g., a response instructs the server to increase the alertlevel) and determined exception characteristics (e.g., the conditionrelates to a high concern biometric attribute). For example, the servermay proceed with the alert notification process if a received alertnotification response indicates that the alert level for a determinedexception should be increased. If the server determines no further alertnotification action or adjustment is required (i.e., determination block508=“No”), the server may conduct an update operation, as describedabove with reference to block 310 in FIG. 3.

In an embodiment, the server may decide to continue with the alertprocess if the current alert is of a particular level. For example, theserver may proceed with the alert notification process when there is noresponse to an alert notification of at least a medium alert level. Inanother embodiment, the server may determine that further alertnotifications are unnecessary when the exception regards less essentialattributes, such as skin pH. In determination block 508, the server maymake its determination using other circumstantial factors as well,including the number of alert notifications and alert level adjustmentsalready conducted for a particular determined exception. For example,the server may determine that an exception regarding a slighttemperature of a child may not require further alert notifications aftertwo notifications were sent in the recent time period.

In another embodiment, administrators of the system may definecontingency actions (e.g., alert action protocols) which influence howthe server will proceed in determination block 508. For example, anadministrator may define a contingency action appended to an alertaction protocol, such as “If alert is LOW, then text J. SMITH. If noresponse, then QUIT.” Such alert action protocols may contain countersas well, which may dictate how many notifications should be sent foreach alert level, action, or determined exception. For example, an alertaction protocol may contain, “If alert is LOW, then text J. SMITH. If noresponse, then RETRY 3 times.”

In block 510, when the server does not receive a terminal response to analert notification but continues to conduct the alert notificationprocess (i.e., determination block 508=“Yes”), the server may adjust thealert level of a determined exception to represent a differentimportance or urgency. In an embodiment, the server may represent analert level adjustment by modifying a system variable engendering thealert level. The server may continue to adjust alert levels (e.g.,lower, escalate) and execute alert notification actions based on theadjusted levels, as described above with reference to block 504.

In an embodiment, in block 510 the server may utilize gradated alertaction protocols with which each successive alert level encompassesintensified alert notification actions. For example, a first, low alertlevel may cause the server to transmit a single SMS text message alertnotification to an alert notification recipient (e.g., an on-siteguardian), encouraging the alert notification recipient to investigatethe exception condition. The next, medium alert level may cause theserver to transmit another alert notification via SMS text message tothe same alert notification recipient, demanding the person toinvestigate the probable exception case. The medium alert level mayinvolve the server sending additional SMS text messages to more alertnotification recipients associated with the alert level (e.g., parents).The high alert level may cause the server to send “EMERGENCY” SMS textalert notifications to all alert notification recipients, includingemergency services (e.g., the fire department).

FIG. 6 illustrates another embodiment method 600 for managing alertnotifications for biometric attribute exceptions. In this embodiment,the server may incorporate machine learning techniques to modify alertnotifications based on notification responses. In determination block502, the server may determine exceptions, as described above withreference to FIG. 5. If no exceptions are detected (i.e., determinationblock 502=“No”), the server may proceed to an update operation, asdescribed above in block 310 in FIG. 3. If the server determines anexception (i.e., determination block 502=“Yes”), in block 604 the servermay establish alert levels and corresponding alert notification actionsbased on the determined exception, such as described above withreference to block 504 in FIG. 5. However, unlike the operations inblock 504, these actions may be prescribed but not executed by theserver.

In block 606, the server may evaluate the prescribed but unexecutedalert notification actions by comparing these actions to experiences ofpreviously executed actions. Previous alert notification experiences maybe stored in a database as described below with reference to block 614.Using the database information, the server may determine expectedresponses based on previous response performances of similarnotification characteristics. For example, if the prescribed mid-levelalert notification action for the determined exception warrants an SMStext message, the server may determine the expected response time as theaverage response time for all previous SMS text messages. In anembodiment, the server may also analyze alert notification recipientsassociated with the prescribed alert notification action and evaluatetheir previous responses to similar circumstances. For example, theserver may determine a recipient's expected response time for alow-level alert notification based on the recipient's previous responsesto similar messages.

In determination block 608, the server may decide if it needs to changethe prescribed alert notification action based on the evaluationsdescribed in block 606. If prescribed alert notification actions areinadequate for a determined exception (i.e., the expected response timeis too long), then the server may need to change those actions. In anembodiment, the server may evaluate the adequacy of the expectedresponses to the prescribed alert notification action using thecharacteristics of the determined exception, such as the alert level.For example, the server may determine that the prescribed SMS textmessage alert notification is inadequate for a high alert levelexception because the expected response time is several hours. Asanother example, the server may determine that the prescribed SMS textmessage alert notification is adequate for a low alert level exceptionas previous similar exceptions for the observed individual have resolvedwithout any responses. In an embodiment, the server may use thresholdsvalues stored within data tables to evaluate the adequacy of theexpected response time for prescribed alert notification actions. Forexample, the server may compare the expected response time of aprescribed low-level alert notification to a data table which listsacceptable response times for each alert level. If the expected responseis within the data table's threshold of acceptable response times forlow level alerts, then the server may deem the prescribed alertnotification action adequate for execution regarding the particulardetermined exception. In an embodiment, the data table may bemulti-dimensional and provide response time thresholds based on manyvariables, such as alert level and message type. In another embodiment,the server may adjust acceptable response time data table values basedon notification experiences. If the server determines the prescribedalert notification action is adequate for the exception and does notneed to be changed (i.e., determination block 608=“No”), the server mayexecute the prescribed alert notification action in block 612.

In block 610, the server may use the stored previous experienceinformation to modify the prescribed alert notification actions when theaction requires change (i.e., determination block 608=“Yes”). In anembodiment, the server may use previous response time values (e.g.,averages) to augment listening durations indicated in alert actionprotocols. For example, over the course of many alert notifications, analert notification recipient's average time to respond to emailnotification may be only three minutes; therefore, the server may changethe prescribed alert notification action to wait for email responsesfrom that recipient for up to three minutes instead of ten minutes. Theserver may also adjust alert notification actions based on the datatables of threshold values as described above with reference todetermination block 608. For example, the server may change a prescribedalert notification action to wait for responses only as long as the waitperiod falls within the threshold for a certain alert level. In anembodiment, a lack of responses to a particular alert notificationaction may be recognized by the server, which may replace theunsuccessful action with an alternative. For example, if a recipient hasconsistently failed to respond to email alert notifications, the servermay instead attempt to contact the recipient via SMS text message. In anembodiment, the server may change the alert notification recipient,transmission method, or any other characteristic of a prescribed alertnotification action.

In block 612, the server may execute the alert notification actionestablished by the operations described in blocks 604-610. The executionof alert notification actions, such as the transmission of SMS textmessages via the server, is described above with reference to block 504in FIG. 5. The server may also detect the receipt of terminal responsesto alert notifications in determination block 506, determine if furtheractions are required for particular alert notifications in determinationblock 508, adjust alert levels in block 510, and continue the method600. The operations in determination blocks 506 and 508 and block 510are as described above with reference to FIG. 5.

In block 614, when there is a terminal response (i.e., determinationblock 506=“Yes”) or if no further response is required (i.e.,determination block 508=“No”), the server may store within a databasespecific information regarding a response to the alert notification. Inrelation to received responses, the server may store characteristics ofthe alert notification, such as the alert level, identity of theobserved individual, and the corresponding determined exception. In anembodiment, alert notification characteristics may include adetermination of whether the alert level of the notification wasescalated or otherwise adjusted from its original alert level. Theserver may also store information whether or not a response was receivedfor a particular alert notification. In an embodiment, the server maystore response times by various recipients (i.e., overseeing parties)for various alert notification characteristics. For example, the servermay record that an alert notification recipient took an hour to respondto a low-level alert regarding a body temperature exception. The servermay store and/or update summary calculations (e.g., running averages)for response times by particular alert notification recipients based onvarious alert notification characteristics. For example, the databasemay contain a running average of the response times by an alertnotification recipient regarding email alert notifications. In anotherembodiment, the server may also track and store combinations of factors,such as notification transmission type and alert notification recipientidentities, that have resulted in particular responses. For example, theserver may determine that an alert notification recipient respondsquickly to SMS text message notifications, and so may record thecombination. When there are no alert notification actions or decisionsto execute, the server may proceed to update operations, such asdescribed above with reference to block 310 in FIG. 3.

The computing unit used in the various embodiments may be any of avariety of commercially available server devices, such as the server 700illustrated in FIG. 7. Such a server 700 typically includes a processor701, and may include multiple processor systems 711, 721, 731, one ormore of which may be or include multi-core processors. The processor 701may be coupled to volatile memory 702 and a large capacity nonvolatilememory, such as a disk drive 703. The server 700 may also include afloppy disc drive, compact disc (CD) or DVD disc drive coupled to theprocessor 701. The server 700 may also include network access ports 704coupled to the processor 701 for establishing data connections with anetwork, such as a local area network coupled to other broadcast systemcomputers and servers.

The various embodiments described above may also be implemented within avariety of wireless biometric sensor devices, such as a wrist watch-typewireless biometric sensor device 800 as illustrated in FIG. 8. Thewireless biometric sensor device 800 may include a processor 802 coupledto an internal memory 804. Internal memory 804 may be volatile ornon-volatile memory, and may also be secure and/or encrypted memory, orunsecure and/or unencrypted memory, or any combination thereof. Theprocessor 802 may also be coupled to a touch screen display 830, such asa resistive-sensing touch screen, capacitive-sensing touch screeninfrared sensing touch screen, or the like. Additionally, the wirelessbiometric sensor device 800 may include a short-range radio signaltransceiver 806 (e.g., a Bluetooth®, Zigbee®, or Peanut® radio) and anantenna 808 for sending and receiving wireless transmissions describedherein. The wireless biometric sensor device 800 may also includephysical buttons 822 and 810 for receiving user inputs. The wirelessbiometric sensor device 800 may also include a vibratory motor 821coupled to the processor 802 to enable the wireless biometric sensordevice 800 to vibrate. The wireless biometric sensor device 800 may alsoinclude various sensors, such as a body temperature sensor 814, a pHsensor 815, a perspiration sensor 816, a blood pressure sensor 817, apulse rate sensor 818, a blood sugar level sensor 819, a blood oxygenlevel sensor 820, and an accelerometer 812 coupled to the processor 802.The wireless biometric sensor device 800 may include a battery 826.

The sensors 814-820 may require sufficient contact with the individualusing the wireless biometric sensor device 800 to enable biometricmeasurements. In an embodiment, sensors 814-820 may be permanentlypositioned within the surface of the wireless biometric sensor devicecasing 827. In another embodiment, sensors 814-820 may be removableunits tethered to the wireless biometric sensor device 800 with cablecapable of enclosing and providing digital information transmission. Asan example, sensors 814-820 may be units extracted from the wirelessbiometric sensor device casing 827, connected to the functionalcircuitry of the wireless biometric sensor device through fiber opticcables, and affixed to the user's skin using adhesive.

The processors 701, 711, 721, 731, 802 may be any programmablemicroprocessor, microcomputer or multiple processor chip or chips thatcan be configured by software instructions (applications) to perform avariety of functions, including the functions of the various embodimentsdescribed above. In some devices, multiple processors may be provided,such as one processor dedicated to wireless communication functions andone processor dedicated to running other applications. Typically,software applications may be stored in the internal memory 702, 804before they are accessed and loaded into the processors 701, 711, 721,731, 802. The processors 701, 711, 721, 731, 802 may include internalmemory sufficient to store the application software instructions. Inmany devices the internal memory may be a volatile or nonvolatilememory, such as flash memory, or a mixture of both. For the purposes ofthis description, a general reference to memory refers to memoryaccessible by the processors 701, 711, 721, 731, 802 including internalmemory or removable memory plugged into the wireless biometric sensordevice and memory within the processors 701, 711, 721, 731, 802themselves.

The foregoing method descriptions and the process flow diagrams areprovided merely as illustrative examples and are not intended to requireor imply that the steps of the various embodiments must be performed inthe order presented. As will be appreciated by one of skill in the artthe order of steps in the foregoing embodiments may be performed in anyorder. Words such as “thereafter,” “then,” “next,” etc. are not intendedto limit the order of the steps; these words are simply used to guidethe reader through the description of the methods. Further, anyreference to claim elements in the singular, for example, using thearticles “a,” “an” or “the” is not to be construed as limiting theelement to the singular.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedherein may be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentinvention.

The hardware used to implement the various illustrative logics, logicalblocks, modules, and circuits described in connection with the aspectsdisclosed herein may be implemented or performed with a general purposeprocessor, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA) orother programmable logic device, discrete gate or transistor logic,discrete hardware components, or any combination thereof designed toperform the functions described herein. A general-purpose processor maybe a microprocessor, but, in the alternative, the processor may be anyconventional processor, controller, microcontroller, or state machine. Aprocessor may also be implemented as a combination of computing devices,e.g., a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration. Alternatively, some steps ormethods may be performed by circuitry that is specific to a givenfunction.

In one or more exemplary aspects, the functions described may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the functions may be stored on ortransmitted over as one or more instructions or code on acomputer-readable medium. The operations of a method or algorithmdisclosed herein may be embodied in a server or processor-executablesoftware module which may reside on a tangible, non-transitorycomputer-readable storage medium. Tangible, non-transitorycomputer-readable storage media may be any available media that can beaccessed by a computer (e.g., a server). By way of example, and notlimitation, such non-transitory computer-readable media may compriseRAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other medium that maybe used to store desired program code in the form of instructions ordata structures and that may be accessed by a computer. Disk and disc,as used herein, includes compact disc (CD), laser disc, optical disc,digital versatile disc (DVD), floppy disk, and blu-ray disc where disksusually reproduce data magnetically, while discs reproduce dataoptically with lasers. Combinations of the above should also be includedwithin the scope of non-transitory computer-readable media.Additionally, the operations of a method or algorithm may reside as oneor any combination or set of codes and/or server processor-executableinstructions on a non-transitory machine readable medium and/orcomputer-readable medium, which may be incorporated into a computerprogram product.

The preceding description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the following claims and theprinciples and novel features disclosed herein.

What is claimed is:
 1. A method of monitoring a population ofindividuals for health and safety, comprising: generating a biometricmodel of nominal and threshold biometric parameters for each individualof the population based on biometric sensor data obtained by one or morebiometric sensors and transmitted wirelessly from a plurality ofwireless biometric sensor devices which are connected to the sensors andcarried by the individuals of the population; receiving, at a server,current biometric sensor data from the plurality of wireless biometricsensor devices; associating, in the server, the current biometric sensordata received from each of the plurality of mobile devices with arespective individual; evaluating the current biometric sensor data forthe individual using the biometric model for that individual;determining an exception condition when the current biometric sensordata is outside of a nominal range of at least one biometric parameterfor the individual; transmitting an alert notification in response todetermining the exception condition; and updating the biometric modelfor the individual based on the current biometric sensor data, whereinthe biometric model for each individual is determined by the serveranalyzing received biometric sensor data for each individual over aperiod of time to calculate average and threshold values for eachbiometric parameter as a function of physical location of eachindividual.
 2. The method of claim 1, wherein the biometric model foreach individual is determined by the server analyzing received biometricsensor data for each individual over a period of time to calculateaverage and threshold values for each biometric parameter as a functionof temporal conditions.
 3. The method of claim 2, further comprising:determining temporal conditions at the time the current biometric sensordata is to the server, wherein evaluating the current biometric sensordata for the individual using the biometric model for that individualcomprises comparing the current biometric sensor data for the individualto nominal ranges of the biometric parameters for that individual intemporal conditions similar to those of the temporal conditions at thetime the current biometric sensor data is transmitted to the server. 4.The method of claim 1, wherein the biometric model for each individualis determined by the server analyzing received biometric sensor data foreach individual over a period of time to calculate average and thresholdvalues for each biometric parameter as a function of scheduledactivities.
 5. The method of claim 4, further comprising: determiningscheduled activities for the population of individuals at the time thecurrent biometric sensor data is transmitted to the server, whereinevaluating the current biometric sensor data for the individual usingthe biometric model for that individual comprises comparing the currentbiometric sensor data for the individual to nominal ranges of thebiometric parameters during activities similar to the scheduledactivities at the time the current biometric sensor data is transmittedto the server.
 6. The method of claim 1, wherein the biometric model foreach individual is determined by the server analyzing received biometricsensor data for each individual over a period of time to calculateaverage and threshold values for each biometric parameter as a functionof atmospheric conditions.
 7. The method of claim 6, further comprising:determining the atmospheric conditions for the population of individualsat the time the current biometric sensor data is transmitted to theserver, wherein evaluating the current biometric sensor data for theindividual using the biometric model for that individual comprisescomparing the current biometric sensor data for the individual tonominal ranges of the biometric parameters for that individual inatmospheric conditions similar to those of the atmospheric conditions atthe time the current biometric sensor data is transmitted to the server.8. The method of claim 1, further comprising: determining the physicallocation of the individuals in the population at the time the currentbiometric sensor data is transmitted to the server, wherein evaluatingthe current biometric sensor data for each respective individual usingthe biometric model for that individual comprises comparing the currentbiometric sensor data for each respective individual to nominal rangesof the biometric parameters for that individual while at the physicallocation at the time the current biometric sensor data is transmitted tothe server.
 9. The method of claim 1, wherein the biometric model foreach individual is determined by the server analyzing received biometricsensor data for each individual over a period of time to identifydependencies between various biometric parameters.
 10. The method ofclaim 1, wherein updating the biometric model for the individualcomprises adjusting nominal and threshold values to represent thecurrent biometric sensor data.
 11. The method of claim 1, whereinupdating the biometric model for the individual comprises adjustingnominal and threshold values to represent information arising fromresponses to alert notifications.
 12. The method of claim 1, furthercomprising determining an exception condition based upon determiningthat the current biometric sensor data describes biometric measurementsthat are outside a nominal range of a biometric parameter in a mannerthat suggests illness or injury.
 13. The method of claim 1, wherein thealert notification is determined by characteristics of the exceptioncondition.
 14. The method of claim 1, wherein transmitting the alertnotification comprises sending one of electronic, symbolic, ortelephonic communications.
 15. The method of claim 1, whereintransmitting the alert notification comprises sending communications tomore than one recipient.
 16. The method of claim 1, further comprisingtransmitting additional alert notifications in response to adjustmentsof alert levels based upon changes in the exception condition.
 17. Themethod of claim 1, wherein the alert notification is determined byevaluating responses to previous alert notifications transmitted inresponse to previous exception conditions similar to the exceptioncondition.
 18. The method of claim 1, wherein the biometric parametersmeasured by the one or more biometric sensors is one or more oftemperature, acceleration, pulse rate, blood pressure, blood oxygenlevel, blood sugar level, pH of skin, and presence of perspiration. 19.A server, comprising: a memory; and a server processor coupled to thememory and configured with server processor-executable instructions toperform operations comprising: generating a biometric model of nominaland threshold biometric parameters for each individual of a populationbased on biometric sensor data obtained from a plurality of wirelessbiometric sensor devices which are attached to the individuals of thepopulation and comprise one or more biometric sensors; receiving currentbiometric sensor data from the plurality of wireless biometric sensordevices; associating the current biometric sensor data received fromeach mobile device with a respective individual; evaluating the currentbiometric sensor data for the individual using the biometric model forthat individual; determining an exception condition when the currentbiometric sensor data is outside of a nominal range of at least onebiometric parameter for the individual; transmitting an alertnotification in response to determining the exception condition; andupdating the biometric model for the individual based on the currentbiometric sensor data, wherein generating the biometric model of nominaland threshold biometric parameters for each individual of a populationbased on biometric sensor data obtained from a plurality of wirelessbiometric sensor devices comprises analyzing received biometric sensordata for each individual over a period of time to calculate average andthreshold values for each biometric parameter as a function of physicallocation of each individual.
 20. The server of claim 19, wherein theserver processor is configured with server processor-executableinstructions to perform operations such that generating a biometricmodel of nominal and threshold biometric parameters for each individualof a population based on biometric sensor data obtained from a pluralityof wireless biometric sensor devices comprises analyzing receivedbiometric sensor data for each individual over a period of time tocalculate average and threshold values for each biometric parameter as afunction of temporal conditions.
 21. The server of claim 20, wherein:the server processor is configured with server processor-executableinstructions to perform operations further comprising determiningtemporal conditions at the time the current biometric sensor data istransmitted to the server; and the server processor is configured withserver processor-executable instructions such that evaluating thecurrent biometric sensor data for the individual using the biometricmodel for that individual comprises comparing the current biometricsensor data for the individual to nominal ranges of the biometricparameters for that individual in temporal conditions similar to thoseof the temporal conditions at the time the current biometric sensor datais transmitted to the server.
 22. The server of claim 19, wherein theserver processor is configured with server processor-executableinstructions to perform operations such that generating a biometricmodel of nominal and threshold biometric parameters for each individualof a population based on biometric sensor data obtained from a pluralityof wireless biometric sensor devices comprises analyzing receivedbiometric sensor data for each individual over a period of time tocalculate average and threshold values for each biometric parameter as afunction of scheduled activities.
 23. The server of claim 22, wherein:the server processor is configured with server processor-executableinstructions to perform operations further comprising determiningscheduled activities for the population of individuals at the time thecurrent biometric sensor data is transmitted to the server; and theserver processor is configured with server processor-executableinstructions such that evaluating the current biometric sensor data forthe individual using the biometric model for that individual comprisescomparing the current biometric sensor data for the individual tonominal ranges of the biometric parameters during activities similar tothe scheduled activities at the time the current biometric sensor datais transmitted to the server.
 24. The server of claim 19, wherein theserver processor is configured with server processor-executableinstructions to perform operations such that generating a biometricmodel of nominal and threshold biometric parameters for each individualof a population based on biometric sensor data obtained from a pluralityof wireless biometric sensor devices comprises analyzing receivedbiometric sensor data for each individual over a period of time tocalculate average and threshold values for each biometric parameter as afunction of atmospheric conditions.
 25. The server of claim 24, wherein:the server processor is configured with server processor-executableinstructions to perform operations further comprising determining theatmospheric conditions for the population of individuals at the time thecurrent biometric sensor data is transmitted to the server; and theserver processor is configured with server processor-executableinstructions such that evaluating the current biometric sensor data forthe individual using the biometric model for that individual comprisescomparing the current biometric sensor data for the individual tonominal ranges of the biometric parameters for that individual inatmospheric conditions similar to those of the atmospheric conditions atthe time the current biometric sensor data is transmitted to the server.26. The server of claim 19, wherein: the server processor is configuredwith server processor-executable instructions to perform operationsfurther comprising determining the physical location of the individualsin the population at the time the current biometric sensor data istransmitted to the server; and the server processor is configured withserver processor-executable instructions such that evaluating thecurrent biometric sensor data for each respective individual using thebiometric model for that individual comprises comparing the currentbiometric sensor data for each respective individual to nominal rangesof the biometric parameters for that individual while at the physicallocation at the time the current biometric sensor data is transmitted tothe server.
 27. The server of claim 19, wherein the server processor isconfigured with server processor-executable instructions to performoperations such that generating a biometric model of nominal andthreshold biometric parameters for each individual of a population basedon biometric sensor data obtained from a plurality of wireless biometricsensor devices comprises analyzing received biometric sensor data foreach individual over a period of time to identify dependencies betweenvarious biometric parameters.
 28. The server of claim 19, wherein theserver processor is configured with server processor-executableinstructions such that updating the biometric model for the individualcomprises adjusting nominal and threshold values to represent thecurrent biometric sensor data.
 29. The server of claim 19, wherein theserver processor is configured with server processor-executableinstructions such that updating the biometric model for the individualcomprises adjusting nominal and threshold values to representinformation arising from responses to alert notifications.
 30. Theserver of claim 19, wherein the server processor is configured withserver processor-executable instructions to perform operations furthercomprising determining an exception condition based upon determiningthat the current biometric sensor data describes biometric measurementsthat are outside a nominal range of a biometric parameter in a mannerthat suggests illness or injury.
 31. The server of claim 19, wherein theserver processor is configured with server processor-executableinstructions to perform operations further comprising determining thealert notification for transmission based on characteristics of theexception condition.
 32. The server of claim 19, wherein the serverprocessor is configured with server processor-executable instructionssuch that transmitting an alert notification in response to determiningthe exception condition comprises sending one of electronic, symbolic,or telephonic communications.
 33. The server of claim 19, wherein theserver processor is configured with server processor-executableinstructions such that transmitting an alert notification in response todetermining the exception condition comprises sending communications tomore than one recipient.
 34. The server of claim 19, wherein the serverprocessor is configured with server processor-executable instructions toperform operations further comprising transmitting additional alertnotifications in response to adjustments of alert levels based uponchanges in the exception condition.
 35. The server of claim 19, whereinthe server processor is configured with server processor-executableinstructions to perform operations further comprising determining thealert notification for transmission by evaluating responses to previousalert notifications transmitted in response to previous exceptionconditions similar to the exception condition.
 36. The server of claim19, wherein the server processor is configured with serverprocessor-executable instructions to perform operations such that thebiometric sensor data evaluated and used in updating the biometric modelcomprises one or more of temperature, acceleration, pulse rate, bloodpressure, blood oxygen level, blood sugar level, pH of skin, andpresence of perspiration.
 37. A server, comprising: means for generatinga biometric model of nominal and threshold biometric parameters for eachindividual of a population based on biometric sensor data obtained froma plurality of wireless biometric sensor devices which are attached tothe individuals of the population and comprise one or more biometricsensors; means for receiving current biometric sensor data from theplurality of wireless biometric sensor devices; means for associatingthe current biometric sensor data received from each mobile device witha respective individual; means for evaluating the current biometricsensor data for the individual using the biometric model for thatindividual; means for determining an exception condition when thecurrent biometric sensor data is outside of a nominal range of at leastone biometric parameter for the individual; means for transmitting analert notification in response to determining the exception condition;and means for updating the biometric model for the individual based onthe current biometric sensor data, wherein the means for generating abiometric model of nominal and threshold biometric parameters for eachindividual of a population based on biometric sensor data obtained froma plurality of wireless biometric sensor devices comprises means foranalyzing received biometric sensor data for each individual over aperiod of time to calculate average and threshold values for eachbiometric parameter as a function of physical location of eachindividual.
 38. The server of claim 37, wherein means for generating abiometric model of nominal and threshold biometric parameters for eachindividual of a population based on biometric sensor data obtained froma plurality of wireless biometric sensor devices comprises means foranalyzing received biometric sensor data for each individual over aperiod of time to calculate average and threshold values for eachbiometric parameter as a function of temporal conditions.
 39. The serverof claim 38, further comprising means for determining temporalconditions at the time the current biometric sensor data is transmittedto the server, wherein means for evaluating the current biometric sensordata for the individual using the biometric model for that individualcomprises means for comparing the current biometric sensor data for theindividual to nominal ranges of the biometric parameters for thatindividual in temporal conditions similar to those of the temporalconditions at the time the current biometric sensor data is transmittedto the server.
 40. The server of claim 37, wherein means for generatinga biometric model of nominal and threshold biometric parameters for eachindividual of a population based on biometric sensor data obtained froma plurality of wireless biometric sensor devices comprises means foranalyzing received biometric sensor data for each individual over aperiod of time to calculate average and threshold values for eachbiometric parameter as a function of scheduled activities.
 41. Theserver of claim 40, further comprising means for determining scheduledactivities for the population of individuals at the time the currentbiometric sensor data is transmitted to the server, wherein means forevaluating the current biometric sensor data for the individual usingthe biometric model for that individual comprises means for comparingthe current biometric sensor data for the individual to nominal rangesof the biometric parameters during activities similar to the scheduledactivities at the time the current biometric sensor data is transmittedto the server.
 42. The server of claim 37, wherein means for generatinga biometric model of nominal and threshold biometric parameters for eachindividual of a population based on biometric sensor data obtained froma plurality of wireless biometric sensor devices comprises means foranalyzing received biometric sensor data for each individual over aperiod of time to calculate average and threshold values for eachbiometric parameter as a function of atmospheric conditions.
 43. Theserver of claim 42, further comprising means for determining theatmospheric conditions for the population of individuals at the time thecurrent biometric sensor data is transmitted to the server, whereinmeans for evaluating the current biometric sensor data for theindividual using the biometric model for that individual comprises meansfor comparing the current biometric sensor data for the individual tonominal ranges of the biometric parameters for that individual inatmospheric conditions similar to those of the atmospheric conditions atthe time the current biometric sensor data is transmitted to the server.44. The server of claim 37, further comprising means for determining thephysical location of the individuals in the population at the time thecurrent biometric sensor data is transmitted to the server, whereinmeans for evaluating the current biometric sensor data for eachrespective individual using the biometric model for that individualcomprises means for comparing the current biometric sensor data for eachrespective individual to nominal ranges of the biometric parameters forthat individual while at the physical location at the time the currentbiometric sensor data is transmitted to the server.
 45. The server ofclaim 37, wherein means for generating a biometric model of nominal andthreshold biometric parameters for each individual of a population basedon biometric sensor data obtained from a plurality of wireless biometricsensor devices comprises means for analyzing received biometric sensordata for each individual over a period of time to identify dependenciesbetween various biometric parameters.
 46. The server of claim 37,wherein means for updating the biometric model for the individualcomprises means for adjusting nominal and threshold values to representthe current biometric sensor data.
 47. The server of claim 37, whereinmeans for updating the biometric model for the individual comprisesmeans for adjusting nominal and threshold values to representinformation arising from responses to alert notifications.
 48. Theserver of claim 37, further comprising means for determining anexception condition comprises means for determining an exceptioncondition based upon determining that the current biometric sensor datadescribes biometric measurements that are outside a nominal range of abiometric parameter in a manner that suggests illness or injury.
 49. Theserver of claim 37, wherein means for determining an exception conditioncomprises means for using characteristics of the exception condition.50. The server of claim 37, wherein means for transmitting an alertnotification in response to determining the exception conditioncomprises means for sending one of electronic, symbolic, or telephoniccommunications.
 51. The server of claim 37, wherein means fortransmitting an alert notification in response to determining theexception condition comprises means for sending communications to morethan one recipient.
 52. The server of claim 37, further comprising meansfor transmitting additional alert notifications in response toadjustments of alert levels based upon changes in the exceptioncondition.
 53. The server of claim 37, further comprising means fordetermining the alert notification for transmission by evaluatingresponses to previous alert notifications transmitted in response toprevious exception conditions similar to the exception condition. 54.The server of claim 37, wherein the biometric sensor data evaluated andused in updating the biometric model comprises one or more oftemperature, acceleration, pulse rate, blood pressure, blood oxygenlevel, blood sugar level, pH of skin, and presence of perspiration. 55.A non-transitory server-readable storage medium having stored thereonserver processor-executable instructions configured to cause a serverprocessor to perform operations for monitoring a population ofindividuals for health and safety, the operations comprising: generatinga biometric model of nominal and threshold biometric parameters for eachindividual of a population based on biometric sensor data obtained froma plurality of wireless biometric sensor devices which are attached tothe individuals of the population and comprise one or more biometricsensors; receiving current biometric sensor data from the plurality ofwireless biometric sensor devices; associating the current biometricsensor data received from each mobile device with a respectiveindividual; evaluating the current biometric sensor data for theindividual using the biometric model for that individual; determining anexception condition when the current biometric sensor data is outside ofa nominal range of at least one biometric parameter for the individual;transmitting an alert notification in response to determining theexception condition; and updating the biometric model for the individualbased on the current biometric sensor data, wherein the generating abiometric model of nominal and threshold biometric parameters for eachindividual of a population based on biometric sensor data obtained froma plurality of wireless biometric sensor devices comprises analyzingreceived biometric sensor data for each individual over a period of timeto calculate average and threshold values for each biometric parameteras a function of physical location of each individual.
 56. Thenon-transitory server-readable storage medium of claim 55, wherein thestored server processor-executable software instructions are configuredto cause a server processor to perform operations such that generating abiometric model of nominal and threshold biometric parameters for eachindividual of a population based on biometric sensor data obtained froma plurality of wireless biometric sensor devices comprises analyzingreceived biometric sensor data for each individual over a period of timeto calculate average and threshold values for each biometric parameteras a function of temporal conditions.
 57. The non-transitoryserver-readable storage medium of claim 56, wherein: the stored serverprocessor-executable software instructions are configured to cause aserver processor to perform operations further comprising determiningtemporal conditions at the time the current biometric sensor data istransmitted to the server; and the stored server processor-executablesoftware instructions are configured to cause a server processor toperform operations such that evaluating the current biometric sensordata for the individual using the biometric model for that individualcomprises comparing the current biometric sensor data for the individualto nominal ranges of the biometric parameters for that individual intemporal conditions similar to those of the temporal conditions at thetime the current biometric sensor data is transmitted to the server. 58.The non-transitory server-readable storage medium of claim 55, whereinthe stored server processor-executable software instructions areconfigured to cause a server processor to perform operations such thatgenerating a biometric model of nominal and threshold biometricparameters for each individual of a population based on biometric sensordata obtained from a plurality of wireless biometric sensor devicescomprises analyzing received biometric sensor data for each individualover a period of time to calculate average and threshold values for eachbiometric parameter as a function of scheduled activities.
 59. Thenon-transitory server-readable storage medium of claim 58, wherein: thestored server processor-executable software instructions are configuredto cause a server processor to perform operations further comprisingdetermining scheduled activities for the population of individuals atthe time the current biometric sensor data is transmitted to the server;and the stored server processor-executable software instructions areconfigured to cause a server processor to perform operations such thatevaluating the current biometric sensor data for the individual usingthe biometric model for that individual comprises comparing the currentbiometric sensor data for the individual to nominal ranges of thebiometric parameters during activities similar to the scheduledactivities at the time the current biometric sensor data is transmittedto the server.
 60. The non-transitory server-readable storage medium ofclaim 55, wherein the stored server processor-executable softwareinstructions are configured to cause a server processor to performoperations such that generating a biometric model of nominal andthreshold biometric parameters for each individual of a population basedon biometric sensor data obtained from a plurality of wireless biometricsensor devices comprises analyzing received biometric sensor data foreach individual over a period of time to calculate average and thresholdvalues for each biometric parameter as a function of atmosphericconditions.
 61. The non-transitory server-readable storage medium ofclaim 60, wherein: the stored server processor-executable softwareinstructions are configured to cause a server processor to performoperations further comprising determining the atmospheric conditions forthe population of individuals at the time the current biometric sensordata is transmitted to the server; and the stored serverprocessor-executable software instructions are configured to cause aserver processor to perform operations such that evaluating the currentbiometric sensor data for the individual using the biometric model forthat individual comprises comparing the current biometric sensor datafor the individual to nominal ranges of the biometric parameters forthat individual in atmospheric conditions similar to those of theatmospheric conditions at the time the current biometric sensor data istransmitted to the server.
 62. The non-transitory server-readablestorage medium of claim 55, wherein: the stored serverprocessor-executable software instructions are configured to cause aserver processor to perform operations further comprising determiningthe physical location of the individuals in the population at the timethe current biometric sensor data is transmitted to the server; and thestored server processor-executable software instructions are configuredto cause a server processor to perform operations such that evaluatingthe current biometric sensor data for each respective individual usingthe biometric model for that individual comprises comparing the currentbiometric sensor data for each respective individual to nominal rangesof the biometric parameters for that individual while at the physicallocation at the time the current biometric sensor data is transmitted tothe server.
 63. The non-transitory server-readable storage medium ofclaim 55, wherein the stored server processor-executable softwareinstructions are configured to cause a server processor to performoperations such that generating a biometric model of nominal andthreshold biometric parameters for each individual of a population basedon biometric sensor data obtained from a plurality of wireless biometricsensor devices comprises analyzing received biometric sensor data foreach individual over a period of time to identify dependencies betweenvarious biometric parameters.
 64. The non-transitory server-readablestorage medium of claim 55, wherein the stored serverprocessor-executable software instructions are configured to cause aserver processor to perform operations such that updating the biometricmodel for the individual comprises adjusting nominal and thresholdvalues to represent the current biometric sensor data.
 65. Thenon-transitory server-readable storage medium of claim 55, wherein thestored server processor-executable software instructions are configuredto cause a server processor to perform operations such that updating thebiometric model for the individual comprises adjusting nominal andthreshold values to represent information arising from responses toalert notifications.
 66. The non-transitory server-readable storagemedium of claim 55, wherein the stored server processor-executablesoftware instructions are configured to cause a server processor toperform operations further comprising determining an exception conditionbased upon determining that the current biometric sensor data describesbiometric measurements that are outside a nominal range of a biometricparameter in a manner that suggests illness or injury.
 67. Thenon-transitory server-readable storage medium of claim 55, wherein thestored server processor-executable software instructions are configuredto cause a server processor to perform operations further comprisingdetermining the alert notification for transmission based oncharacteristics of the exception condition.
 68. The non-transitoryserver-readable storage medium of claim 55, wherein the stored serverprocessor-executable software instructions are configured to cause aserver processor to perform operations such that transmitting an alertnotification in response to determining the exception conditioncomprises sending one of electronic, symbolic, or telephoniccommunications.
 69. The non-transitory server-readable storage medium ofclaim 55, wherein the stored server processor-executable softwareinstructions are configured to cause a server processor to performoperations such that transmitting an alert notification in response todetermining the exception condition comprises sending communications tomore than one recipient.
 70. The non-transitory server-readable storagemedium of claim 55, wherein the stored server processor-executablesoftware instructions are configured to cause a server processor toperform operations further comprising transmitting additional alertnotifications in response to adjustments of alert levels based uponchanges in the exception condition.
 71. The non-transitoryserver-readable storage medium of claim 55, wherein the stored serverprocessor-executable software instructions are configured to cause aserver processor to perform operations further comprising determiningthe alert notification for transmission by evaluating responses toprevious alert notifications transmitted in response to previousexception conditions similar to the exception condition.
 72. Thenon-transitory server-readable storage medium of claim 55, wherein thestored server processor-executable software instructions are configuredto cause a server processor to perform operations such that thebiometric sensor data evaluated and used in updating the biometric modelcomprises one or more of temperature, acceleration, pulse rate, bloodpressure, blood oxygen level, blood sugar level, pH of skin, andpresence of perspiration.
 73. A system, comprising: a server comprising:a memory; and a server processor coupled to the memory and configuredwith server processor-executable instructions to perform operationscomprising: generating a biometric model of nominal and thresholdbiometric parameters for each individual of a population based onbiometric sensor data obtained from a plurality of wireless biometricsensor devices which are attached to the individuals of the populationand comprise one or more biometric sensors; receiving current biometricsensor data from the plurality of wireless biometric sensor devices;associating the current biometric sensor data received from each mobiledevice with a respective individual; evaluating the current biometricsensor data for the individual using the biometric model for thatindividual; determining an exception condition when the currentbiometric sensor data is outside of a nominal range of at least onebiometric parameter for the individual; transmitting an alertnotification in response to determining the exception condition; andupdating the biometric model for the individual based on the currentbiometric sensor data, wherein the generating a biometric model ofnominal and threshold biometric parameters for each individual of apopulation based on biometric sensor data obtained from a plurality ofwireless biometric sensor devices comprises analyzing received biometricsensor data for each individual over a period of time to calculateaverage and threshold values for each biometric parameter as a functionof physical location of each individual.
 74. The system of claim 73,wherein the server processor is configured with serverprocessor-executable instructions to perform operations such thatgenerating a biometric model of nominal and threshold biometricparameters for each individual of a population based on biometric sensordata obtained from a plurality of wireless biometric sensor devicescomprises analyzing received biometric sensor data for each individualover a period of time to calculate average and threshold values for eachbiometric parameter as a function of temporal conditions.
 75. The systemof claim 74, wherein: the server processor is configured with serverprocessor-executable instructions to perform operations furthercomprising determining temporal conditions at the time the currentbiometric sensor data is transmitted to the server; and the serverprocessor is configured with server processor-executable instructionssuch that evaluating the current biometric sensor data for theindividual using the biometric model for that individual comprisescomparing the current biometric sensor data for the individual tonominal ranges of the biometric parameters for that individual intemporal conditions similar to those of the temporal conditions at thetime the current biometric sensor data is transmitted to the server. 76.The system of claim 73, wherein the server processor is configured withserver processor-executable instructions to perform operations such thatgenerating a biometric model of nominal and threshold biometricparameters for each individual of a population based on biometric sensordata obtained from a plurality of wireless biometric sensor devicescomprises analyzing received biometric sensor data for each individualover a period of time to calculate average and threshold values for eachbiometric parameter as a function of scheduled activities.
 77. Thesystem of claim 76, wherein: the server processor is configured withserver processor-executable instructions to perform operations furthercomprising determining scheduled activities for the population ofindividuals at the time the current biometric sensor data is transmittedto the server; and the server processor is configured with serverprocessor-executable instructions such that evaluating the currentbiometric sensor data for the individual using the biometric model forthat individual comprises comparing the current biometric sensor datafor the individual to nominal ranges of the biometric parameters duringactivities similar to the scheduled activities at the time the currentbiometric sensor data is transmitted to the server.
 78. The system ofclaim 73, wherein the server processor is configured with serverprocessor-executable instructions to perform operations such thatgenerating a biometric model of nominal and threshold biometricparameters for each individual of a population based on biometric sensordata obtained from a plurality of wireless biometric sensor devicescomprises analyzing received biometric sensor data for each individualover a period of time to calculate average and threshold values for eachbiometric parameter as a function of atmospheric conditions.
 79. Thesystem of claim 78, wherein: the server processor is configured withserver processor-executable instructions to perform operations furthercomprising determining the atmospheric conditions for the population ofindividuals at the time the current biometric sensor data is transmittedto the server; and the server processor is configured with serverprocessor-executable instructions such that evaluating the currentbiometric sensor data for the individual using the biometric model forthat individual comprises comparing the current biometric sensor datafor the individual to nominal ranges of the biometric parameters forthat individual in atmospheric conditions similar to those of theatmospheric conditions at the time the current biometric sensor data istransmitted to the server.
 80. The system of claim 73, wherein: theserver processor is configured with server processor-executableinstructions to perform operations further comprising determining thephysical location of the individuals in the population at the time thecurrent biometric sensor data is transmitted to the server; and theserver processor is configured with server processor-executableinstructions such that evaluating the current biometric sensor data foreach respective individual using the biometric model for that individualcomprises comparing the current biometric sensor data for eachrespective individual to nominal ranges of the biometric parameters forthat individual while at the physical location at the time the currentbiometric sensor data is transmitted to the server.
 81. The system ofclaim 73, wherein the server processor is configured with serverprocessor-executable instructions to perform operations such thatgenerating a biometric model of nominal and threshold biometricparameters for each individual of a population based on biometric sensordata obtained from a plurality of wireless biometric sensor devicescomprises analyzing received biometric sensor data for each individualover a period of time to identify dependencies between various biometricparameters.
 82. The system of claim 73, wherein the server processor isconfigured with server processor-executable instructions such thatupdating the biometric model for the individual comprises adjustingnominal and threshold values to represent the current biometric sensordata.
 83. The system of claim 73, wherein the server processor isconfigured with server processor-executable instructions such thatupdating the biometric model for the individual comprises adjustingnominal and threshold values to represent information arising fromresponses to alert notifications.
 84. The system of claim 73, whereinthe server processor is configured with server processor-executableinstructions to perform operations further comprising determining anexception condition based upon determining that the current biometricsensor data describes biometric measurements that are outside a nominalrange of a biometric parameter in a manner that suggests illness orinjury.
 85. The system of claim 73, wherein the server processor isconfigured with server processor-executable instructions to performoperations further comprising determining the alert notification fortransmission based on characteristics of the exception condition. 86.The system of claim 73, wherein the server processor is configured withserver processor-executable instructions such that transmitting an alertnotification in response to determining the exception conditioncomprises sending one of electronic, symbolic, or telephoniccommunications.
 87. The system of claim 73, wherein the server processoris configured with server processor-executable instructions such thattransmitting an alert notification in response to determining theexception condition comprises sending communications to more than onerecipient.
 88. The system of claim 73, wherein the server processor isconfigured with server processor-executable instructions to performoperations further comprising transmitting additional alertnotifications in response to adjustments of alert levels based uponchanges in the exception condition.
 89. The system of claim 73, whereinthe server processor is configured with server processor-executableinstructions to perform operations further comprising determining thealert notification for transmission by evaluating responses to previousalert notifications transmitted in response to previous exceptionconditions similar to the exception condition.
 90. The system of claim73, wherein the server processor is configured with serverprocessor-executable instructions to perform operations such that thebiometric sensor data evaluated and used in updating the biometric modelcomprises one or more of temperature, acceleration, pulse rate, bloodpressure, blood oxygen level, blood sugar level, pH of skin, andpresence of perspiration.